Mechanical engineering

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/4940

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Computational Design and Modeling of Vitrimers
(2026-04-20) Zheng, Yiwen; Vashisth, Aniruddh
Vitrimers are an emerging class of sustainable polymers that consist of dynamic covalent adaptive networks. The ability of their polymer chains to attach and detach from one another through bond exchange reactions provides them with self-healing capabilities. However, the widespread application of vitrimers across various industries is currently limited by a lack of understanding of their molecular structures at the atomistic scale, primarily due to two key aspects. First, the limited choice of available monomers constrains current vitrimer chemistries, thereby restricting their thermomechanical properties. Second, existing experimental and simulation methods fall short in capturing the intricate bond exchange reactions and subsequent network rearrangement, which in turn limits our understanding of the viscoelastic properties of vitrimers. To address these challenges, this dissertation presents an integrated molecular dynamics (MD) - machine learning (ML) framework for the design and investigation of vitrimers at the atomistic scale. For training the ML models, a diverse vitrimer dataset of one million chemistries is curated. The glass transition temperature (Tg) for 8,424 of these chemistries is then calculated using highthroughput MD simulations calibrated by a Gaussian process model. A novel variational autoencoder model, which employs dual graph encoders and a latent dimension overlapping scheme, is developed and trained on this MD data. This design allows for the individual representation of multi-component vitrimers. High accuracy and efficiency of this framework are demonstrated by its ability to discover novel vitrimers with desirable Tg values that are outside the training regime. To experimentally validate the effectiveness of the framework, vitrimer chemistries are generated with a target Tg of 323 K. By incorporating chemical intuition, a novel vitrimer with a Tg of 311–317 K is successfully synthesized, which experimentally demonstrated both healability and flowability. The proposed framework offers a promising tool for polymer chemists to design and synthesize novel, sustainable polymers for various applications. Leveraging the same MD data, we train and benchmark seven ML models with six different feature representations for the prediction of Tg. By averaging the predicted Tg values from different models, an ensemble approach is shown to outperform individual models, enabling accurate and efficient property prediction on unlabeled datasets. Two novel vitrimers are identified and synthesized, exhibiting experimentally validated higher Tg than existing bifunctional transesterification vitrimers, along with demonstrated healability. This work demonstrates the potential of using MD data to train predictive ML models in the absence of sufficient experimental data, thereby enabling the discovery of novel, synthesizable polymer chemistries with a wide range of desirable properties. To realistically capture the complete reaction pathways of bond exchange reactions in vitrimers, we extend the Accelerated Reactive Molecular Dynamics (ReaxFF) technique. This extended framework enables a more accurate representation of vitrimer viscoelastic behavior at the molecular level. Bayesian optimization is used to select force field parameters within the Accelerated ReaxFF framework. Additionally, an empirical function is proposed to model temperature dependency, which allows for the control of reaction probabilities under varying temperatures. The extended framework is then used to simulate the non-isothermal creep behavior of vitrimers under various applied stress levels, heating rates, and numbers of reactions. The simulation results show good agreement with experimental findings in the literature, which validates the framework’s robustness. Using the Accelerated ReaxFF framework, we investigate the interplay between vitrimer chemistry, Tg, and healability. By quantifying the mobility of reactive atoms through mean square displacement calculations, we provide atomistic insights into experimental observations which offer guidance for designing vitrimers with better healability.
A Novel Plasma Treatment Based Solid-State Foaming Process for Producing Skinless Foams
(2026-04-20) Deshpande, Shaunak Vivek; Meza, Lucas R
Solid‑state foaming (SSF) reliably produces micro‑ and nanocellular polymer structures but consistently forms a dense, unfoamed surface layer (skin) due to CO₂ desorption near free surfaces. This work investigates novel atmospheric‑pressure plasma jet (APPJ) processing as a surface‑treatment technique to overcome this limitation and enable skinless polymer foams. Using localized, rapid, and cyclic surface heating combined with charged‑species interaction, APPJ treatment activates nucleation in the near‑surface region without heating the entire specimen, allowing cellular structures to extend to the surface.Direct plasma foaming of CO₂‑saturated PC, PEI, PEEK, and COC demonstrates that the APPJ process produces skinless foams across both amorphous and semi‑crystalline polymers. Process parameters such as stand‑off distance, flow rate, and number of passes control surface temperatures and foamed‑layer depth. Even under reduced CO₂ availability, either through lower saturation pressures or extended desorption times, plasma foaming continues to yield skinless microcellular structures, highlighting the role of localized high‑temperature surface heating in overcoming near‑surface gas depletion. Mechanistic studies decouple thermal and non‑thermal plasma effects. Etching is negligible compared to typical skin thicknesses, while electron‑rich plasma conditions are essential for uniform surface porosity. Temperature‑matched nozzle experiments show that electrons and reactive species reduce the near‑surface nucleation barrier, whereas heat conduction governs bulk foaming. Complementary unidirectional heating experiments further confirm that surface‑selective thermal gradients can initiate subsurface cell growth but do not replicate the uniform porosity achieved by plasma. Building on these findings, a post-processing technique of selective plasma re-foaming is developed for prefabricated PEI nanofoams. Partial CO₂ resaturation of the dense skin followed by APPJ treatment produces a porous, accessible surface while preserving the core morphology and minimizing warping. Finally, the work extends SSF to epoxy‑based vitrimers, demonstrating low‑temperature CO₂ foaming and catalyst‑dependent control of cellular architecture. Together, these results establish atmospheric‑pressure plasma treatment as a robust, surface‑enabled pathway for producing skinless and, in thin COC films, fully open‑cellular foams. The work provides a mechanistic framework linking localized cyclic heating, plasma–polymer interactions, and subsurface cell growth and broadens the range of polymer systems, including dynamic networks, that can be processed using solid‑state foaming.
Ergodic Graph Exploration via Markov Chain for Active Robotic Information Acquisition
(2026-04-20) Wong, Benjamin; Devasia, Santosh; Banerjee, Ashis G
Many robotic applications can be considered as information acquisition, including surveillance,environmental monitoring, disaster response, and robotic learning. These tasks are often a combination of being repetitive, time consuming, time sensitive, or dangerous, which are unsuitable for human to perform. Specifically, this work consider inspection of confined spaces as the primary example. This work presents a semi-autonomous robotic system for assisting human to perform inspection in such hazardous environment. Challenges arise for robots to operate autonomously in these cluttered, poorly illuminated environment with complex connectivity such as localization, mapping, and navigation. Teleoperation also poses challenge as communication is limited with the confined space being enclosed in large metallic structures. This work focus on autonomous inspection with regard to foreign object debris (FOD) detection. First, an statistical FOD detection method is presented, accounting for mapping uncertainty in various location of the tank. The result is verified by the operator at the end of each inspection session. Second a hierarchical planning method is presented for optimizing the detection rate of FOD while handling the complex connectivity and limited navigation capability. Last, the planning method is generalized to a multi-robot system for collecting information in a large complex environment.
Characterization of Tribological Properties of Ti-6Al-4V Fabricated by Electron Beam Powder Bed Fusion
(2026-04-20) Bin Abdullah, Mohammad Sayem; Mamidala, Ramulu
Titanium alloys, particularly Ti-6Al-4V, are widely used in aerospace, biomedical, and other high-performance industries due to their exceptional strength-to-weight ratio, corrosion resistance, and biocompatibility. With the advent of metal additive manufacturing (AM), Electron Beam Powder Bed Fusion (EB-PBF) has emerged as a promising technology for fabricating Ti-6Al-4V parts with complex geometries and minimal residual stress with no gas interruption inside the build chamber. The mechanical properties, such as tensile strength and fatigue strength of EB-PBF Ti-6Al-4V are comparable to conventionally processed Ti-6Al-4V. However, limited research exists on its tribological performance of EB-PBF Ti-6Al-4V, specifically, wear and erosion behavior, in relation to process-induced variability and powder reuse. While EB-PBF components are adopted in wear-critical structures in aerospace, biomedical, and possibly oil-and-gas industries, limited knowledge of its tribological properties remains a major drawback.This dissertation addresses these critical knowledge gaps by providing a comprehensive investigation of the tribological behavior of EB-PBF Ti-6Al-4V, focusing on the effects of intra-build variability (orientation, height, radial location) and powder reuse. To address these, this dissertation presents a holistic study of tribological response of EB-PBF Ti-6Al-4V through sliding wear tests and erosion wear tests. The sliding wear tests included a ball-on-disc test against steel counterpart according to ASTM G99, rotary abrasion test against ceramic abrasive particle according to ASTM F1978, and a brief scratch test against steel ball. The erosive (erosion) wear test was conducted against silica and alumina particles according to ASTM G76. Wear tests were conducted in both as-built and machined surface conditions. Key tribological metrics such as specific wear rate, friction coefficient, erosion resistance, and wear depth were quantified through sliding wear and erosion tests. Surface roughness, microstructure, and sub-surface microhardness were characterized by understanding the wear mechanisms under varying conditions. In the as-built conditions, the specific wear rate is 7.63-8.74 × 10^(-3) mm^3/Nm against silicon carbide and 5.44-6.25 × 10^(-3) mm^3/Nm against alumina using 12.5 mm wide abrasive strip in the rotary abrasion test under 19.62 N load. In the machined condition, such values are 7.51-8.18 ×10^(-3) mm^3/Nm against silicon carbide and 4.96-5.54 ×10^(-3) mm^3/Nm against alumina. The specific wear rate ranges from 3.8 × 10⁻⁴ to 8.8 × 10⁻⁴ mm³/Nm against steel (9.5 mm diameter) in ball-on-disc test under 10 N load in machined conditions, while the average rate is 5.8 × 10^(-4) mm^3/Nm for all orientations. The wear rate is higher in the rotary abrasion test compared to the ball-on-disc test due to larger contact area. The friction coefficient range is 0.32-0.40 and 0.26-0.45, respectively for as-built and machined conditions. As-built specimens show orientation-dependent wear and frictional behavior, primarily influenced by surface roughness and microstructural anisotropy. Machining significantly reduces this variability, leading to more uniform wear response. Erosion tests indicate ductile erosion mechanisms dominated by micro-cutting and ploughing, with the angle of particle impingement and abrasive type (e.g., alumina vs. silica) significantly affecting material loss. The numerical erosion resistance of EB-PBF Ti-6Al-4V against silica is 1.49-1.84 × 10^(-4) mg/mg, and against alumina is 3.24×10^(-4) mg/mg in as-built and 5.51×10^(-4) mg/mg in machined conditions. Powder reuse increases surface hardness due to elevated oxygen content and lower abrasive wear rate. Tribological properties are sensitive to build orientation and surface condition. This dissertation also explores microstructural evolution and the variation of microhardness and microstructure with thickness. The machinability of EB-PBF Ti-6Al-4V has been assessed, and optimal face milling parameters are proposed to remove surface asperities while minimizing cutting forces and improving surface finish. The cutting coefficient of EB-PBF Ti-6Al-4V is 2016.29 N/mm^2. This dissertation presents a holistic framework to understand the tribological behavior of EB-PBF Ti-6Al-4V, contributing critical insights for its reliable use in wear-critical applications and supporting broader industrial adoption.
Computational processing of open-top light-sheet microscopy datasets for guiding treatment decisions
(2026-04-20) Gao, Gan; Liu, Jonathan T.C.
Slide-based histopathology for biopsies or surgical specimens is the gold standard for guiding treatment decisions. Examples include intraoperative assessment of resected tissue margins based on frozen section analysis and diagnosis and patient prognosis using FFPE slides. However, slide-based microscopic pathology has many limitations, including severe under-sampling, ambiguous 2D visualization of 3D morphologies, slow turnaround times, and destruction of valuable specimens. To address these limitations, open-top light-sheet (OTLS) microscopy has been developed for non-destructive slide-free pathology of clinical specimens. This technology enables rapid imaging of intact tissues at high resolution in 2D or 3D, providing the same level of detail as traditional pathology. To narrow the gaps to clinical adoption, this dissertation focuses on developing computational processing approaches and optimizing OTLS imaging workflows for ex-vivo surgical guidance and non-destructive 3D pathology. Specifically, for the application of surgical guidance, I demonstrate 1) the ability to generate false-colored H&E mimicking images of freshly excised surgical margin surfaces stained for < 1 min with a single fluorophore, 2) rapid OTLS surface imaging at a rate of 15min/cm^2 followed by real-time post-processing of datasets within RAM at a rate of 5min/cm^2, and 3) rapid digital surface extraction to account for topological irregularities. I show that the image quality generated by our rapid surface-histology method in an intraoperative-acceptable timeframe approaches that of gold-standard archival histology. Furthermore, for the application of rendering final diagnostic determinations, the ability to effectively guide pathologists through spatially heterogeneous 3D pathology datasets is critical. To this end, I developed a context-aware deep learning triage approach that automatically identifies the highest-risk 2D slices within 3D volumetric tissue, enabling time-efficient pathologist evaluation. The AI-triaged 3D pathology workflow is implemented for two clinical scenarios, triage of higher-grade prostate cancer as well as screening of esophageal dysplasia/cancer for Barrett's esophagus patients, achieving area-under-curves (AUCs) >0.9 for identifying high-risk slices in both cases. Preliminary clinical validations with panels of Genitourinary and Gastrointestinal pathologists show that AI-triaged 3D pathology improves the identification of higher-grade malignancies compared to standard-of-care 2D slide-based pathology.
Manufacturing, Processing and Mechanics of Fiber Reinforced Vitrimer Composites
(2026-02-05) Nandi, Ankush; Vashisth, Aniruddh
Fiber-reinforced polymers (FRPs) are increasingly replacing metals in industries such as automotive and aerospace due to their excellent strength-to-weight ratios. This trend is expected to grow over the next decade as FRPs offer significant performance advantages. Typically, FRPs are composed of reinforcing fibers (carbon or glass) embedded in a polymeric matrix, which can be thermoplastic or thermosetting. The matrix and the inclusions in composite play a critical role in determining their thermal and mechanical properties. FRPs face limitations such as brittleness, lower fatigue and fracture life, and poor recyclability, which can hinder their broader adoption. Addressing these issues, research has recently shifted towards enhancing the sustainability and circularity of FRPs, aligning with the United Nations Sustainable Development Goals. A promising solution lies in the development of vitrimers, a novel class of polymers with repeated healing capabilities through external stimulus, which addresses the above shortcomings of conventional thermosets and thermoplastics. Along these lines, vitrimers are an exciting new type of polymer with a dynamic polymeric network; this enables these polymers to rearrange their molecular structure to achieve repeated healing. Although various chemistries have been developed for vitrimers, little work has been done on manufacturing vitrimer composites and their resulting mechanical properties. This research explores the manufacturing, mechanics, and processing of vitrimer-based composites through two key approaches. The first focuses on ultrasonic welding as a sustainable and cost-effective method for joining vitrimer composites, while the second investigates the low velocity impact (LVI) and compression after impact (CAI) behavior of vitrimer-based carbon fiber composites. The first part of the dissertation proposes a hypothesis that ultrasonic welding can be used to assemble and weld fiber-reinforced vitrimer composites with thermosetting backbones. Ultrasonic welding was used to weld vitrimer-based composites by leveraging the dynamic bond exchange reactions in transesterification vitrimer systems. Mechanical vibrational energy facilitates these bond exchanges, resulting in strong welds with promising mechanical properties. Ultrasonic welding offers significant advantages over traditional oven curing methods, requiring less energy and time, making it a green and efficient assembly technique. Experimental results revealed that vitrimer-based composites exhibit excellent weld strength and cohesive failure at lap shear interfaces, with performance influenced by catalyst concentration. Carbon fiber-reinforced vitrimer composites outperformed glass fiber-reinforced ones, demonstrating superior weldability and mechanical properties. This work provided insights into the mechanisms of ultrasonic welding, including polymer chain diffusion and bond exchange reactions, paving the way for energy-efficient and sustainable industrial applications. The second part of this dissertation focuses on the hypothesis that low energy, out-of-plane impact damage in fiber composites can be healed by reversing matrix related damage in fiber composites, and residual strength can be retained. To examine this hypothesis, the second aspect of the dissertation focuses on the compression after impact (CAI) behavior of vitrimer-based carbon fiber composites, addressing a critical limitation of FRPs: their susceptibility to damage from barely visible low-velocity (BVID) impacts. Such impacts, common during manufacturing or service, cause delamination at the fiber-matrix interface, significantly reducing residual compressive strength and potentially leading to catastrophic failures while in service. Conventional approaches, such as adding nanomaterials, microcapsules, or Z-pinning, improve damage tolerance but often increase weight and reduce sustainability. This research explores the inherent viscoelasticity of vitrimer matrices to heal impact damage and restore residual strength when heated beyond topology freezing temperature. Healing has been carried out using a heat press. Post impact and after healing damage assessment of composites has been done using computed tomography following CAI. 3D Digital image correlation to understand the out of plane buckling characteristics is also carried out in parallel along with CAI. Results, assessed via CAI testing indicate promising strength retention compared to pristine composites for heat press healed composites, demonstrating the potential of vitrimers for sustainable and high-performance composite applications. Apart from the two key research areas on vitrimer based fiber reinforced composites as discussed above a chapter is also dedicated to the manufacturing of vitrimer based foams using solid state foaming process, using carbon dioxide as a blowing agent. This novel processing route enables the tuning of foam microstructure via catalyst content, and showcases a recyclable, reprocessable thermoset foam concept. The findings open opportunities for lightweight vitrimer materials where microcellular architecture is combined with the advantages of dynamic covalent chemistry, relevant to sustainable foams and advanced dynamic polymer networks.
An Efficient and Effective Electromagnetic Rewarming Platform for Cryopreservation of Large-Volume Biomaterials
(2026-02-05) Wang, Ziyuan; Gao, Dayong
Cryopreservation is the primary method for long-term storage of biological materials, ranging from small cellular samples to larger tissues and whole organs. Its key advantage is enabling biobanking, which is vital for advancing biomedical research in areas such as cell therapy, tissue engineering, and organ transplantation, and ultimately improving the quality of life for millions of people worldwide. However, cryopreservation has not been widely adopted in large-scale industrial applications or routine clinical practice, especially for large samples such as tissues and organs. This limitation arises because there is currently no commercially available or widely accepted heating technology capable of reliably rewarming large cryopreserved samples while maintaining their viability and functionality. Evaluating the performance of heating in cryopreservation hinges on two key factors: heating rate and uniformity. A higher heating rate can reduce the required cryoprotective agent (CPA) concentration, thereby decreasing the toxicity associated with CPA solutions. However, this increased rate often leads to greater thermal stress due to larger temperature gradients, which is a significant concern with traditional conductive or convective heating methods. This issue becomes more pronounced as the sample size increases. Consequently, conventional heating technologies can only achieve limited heating rates for small samples and are not suitable for larger ones due to these constraints. Multiple rewarming modalities are under active development, including nanowarming, Joule heating, laser heating, and dielectric heating, each with distinct advantages and trade-offs. This dissertation focuses on dielectric heating and, specifically, the design, improvement, and validation of a single-mode electromagnetic resonance (SMER) system. This work comprises four innovations: (i) design of a SMER rewarming system; (ii) a control strategy to improve SMER rewarming performance; (iii) optimization of the vitrification solution for SMER rewarming; and (iv) design of a high-power SMER rewarming system aimed at future human organ cryopreservation. This work first developed a single-mode electromagnetic resonance (SMER) rewarming platform for large-volume cryopreservation. The system integrates five subsystems: RF generation (signal generator plus high-power amplifier), control module, real-time monitoring (fiber-optic thermometry and reflected-power sensing), safety module (circulator, dummy load, directional coupler), and the SMER cavity applicator. The dimensions of the SMER cavity applicator was derived from Maxwell’s equations and tuned to the TE101 mode at 434 MHz to place an electric-field antinode at the cavity center for controllable dielectric heating. Finite-element electromagnetic-thermal modeling (COMSOL) predicted resonance at 434.8 MHz and quantified absorbed power density, confirming strong, center-focused fields. Bench validation used a 25 mL cryoprotectant sample (10% DMSO + 0.25 M trehalose) rewarmed from −80 °C to 0 °C. Measured and simulated temperature histories agreed closely. These results validated the design of the SMER rewarming system, demonstrating its viability as a path toward tissue- and organ-scale rewarming. The work then addressed one of the significant challenges in SMER systems, resonance tracking, because efficient conversion of electromagnetic energy to heat occurs only at resonance. Even slight detuning yields very low heating rates (approaching those achieved by air convection) and high reflected power, posing risks to operators and sensitive devices. Previous resonance-tracking approaches relied on experienced operators or trade-off methods (e.g., intermittent feedback control). To address this challenge, extremum seeking control (ESC), an adaptive control approach, was implemented. Compared with manual control, ESC increased the heating rate by 16.8% and significantly reduced risks associated with manual operation, including potential equipment damage during operator training or from occasional procedural errors. ESC also delivered adequate heating rate and uniformity at a 25 mL scale, surpassing the capabilities of conventional water-bath rewarming. To further improve SMER rewarming performance, I built a dielectric measurement system to characterize and optimize vitrification solutions. Temperature-dependent permittivity and loss (−150 to 0 °C) were measured by the cavity perturbation method, with broadband (100–1,500 MHz) profiling via an open-ended coaxial probe to guide vitrification solution choice and frequency-specific optimization at 434 MHz. DPVP exhibited a high dielectric constant and a loss peak near −40 °C that helped limit thermal runaway, but VS55 was selected for 50 mL scaling. To enhance absorption, multi-walled carbon nanotubes (MWCNTs) were dispersed, their concentration verified by TGA, and 0.5 wt% was identified as optimal for VS55 based on the loss tangent at 434 MHz. Jurkat cell tests indicate increased toxicity during prolonged ice exposure but acceptable post-thaw recovery under standard protocols. In 50 mL experiments, VS55 under the 400 W ESC-SMER system failed to exceed the CWR (center/edge 47.59/45.53 °C min⁻¹), whereas 0.5 % MWCNT + VS55 achieved 84.52/99.67 °C min⁻¹, surpassing the CWR and enabling successful, uniform rewarming. Together, the dielectric-property mapping and nanoparticle augmentation provide a practical route to scaling SMER rewarming capability to 50 mL volume samples. To overcome the 400 W ceiling of a signal-generator-plus-amplifier setup, this work adopted a 3-kW solid-state RF source operating at 900 - 930 MHz and engineer a TE101 rectangular cavity (915 MHz), with ESC-based resonance tracking, monitoring, and safety modules. At 1 kW, 25 mL VS55 achieves center/edge rewarming rates of 308.25/340.10 °C·min⁻¹, 49.5%/75.0% higher than the prior 400 W, 434 MHz SMER system with acceptable uniformity. Biological tests on 12 mL Jurkat suspensions show post-thaw recovery of 71.4 ± 5.59% (1 kW SMER rewarming) vs 48.5 ± 13.5% (water-bath rewarming), indicating that better rewarming performance improves the viability of cryopreserved samples. Current limits on sample volume (< 25 mL) stem from operating frequency (the 900–930 MHz band reduces penetration depth and shifts with loading), generator tuning range (volume-dependent detuning below 900 MHz), and elevated reflections that constrain the delivered power. Future work targets lower-frequency solid-state sources, cavity retuning to keep loaded resonance within the band, and improved matching to unlock the full 3 kW and scale beyond 100 mL. This dissertation also outlines plans for future improvements, focusing on optimization of the high-power SMER rewarming system, including resonant cavity design, power delivery, and customization of a solid-state generator. In addition, cryopreservation studies on animal organs will be conducted to further evaluate the system’s capabilities.
Robust and Efficient 6D Object Pose Estimation Using Color Information
(2026-02-05) Yang, Xingjian; Banerjee, Ashis G.
6D object pose estimation, a fundamental computer vision task, has seen remarkable progress driven by deep learning advancements and growing demands across various applications. The challenge lies in determining objects' 3D orientation and position from 2D images while handling complications such as occlusions, cluttered environments, and varying lighting conditions. While traditional methods using geometric features and templates face limitations with texture-less objects and complex scenes, deep learning approaches have significantly improved accuracy and efficiency. These approaches include direct pose regression, keypoint detection, and hybrid methods, particularly addressing the synthetic-to-real domain gap. Current developments focus on end-to-end trainable frameworks capable of efficient multi-object handling and real-time operation from RGB inputs. The field continues to evolve with innovations in data augmentation, network architectures, self-supervised learning from synthetic data, and weakly supervised approaches using 2D annotations. A particular emphasis is placed on mobile computing platforms and real-time performance capabilities, as many applications require processing at the edge with limited network resources. First, we propose the Sparse Color-Code Net (SCCN). It represents an innovative three-stage pipeline designed specifically for real-time 6D object pose estimation, capable of handling both single and multiple objects efficiently. At its core, SCCN employs a unique color-code representation system that enables neural networks to effectively learn and memorize correspondences between object points and their associated colors. The framework's architecture consists of three key stages: First, it utilizes Sobel filters to extract sparse contours that capture essential surface details. These contours, along with the input image, are processed through a UNet architecture for object segmentation and bounding box detection. The second stage involves another UNet that performs pixel-level color-code regression on cropped object patches, establishing crucial 2D-3D correspondences while incorporating a novel symmetry mask to address object ambiguities. In the final stage, color-code pixels are strategically selected based on the contours and transformed into a 3D point cloud, with the PnP algorithm generating the definitive 6D pose estimate. SCCN's efficiency is particularly noteworthy, achieving impressive performance metrics of 19 FPS and 6 FPS on the LINEMOD and LINEMOD Occlusion datasets respectively when running on an NVIDIA Jetson AGX Xavier platform. This performance, combined with its ability to maintain high estimation accuracy and handle symmetric objects effectively, positions SCCN as a significant advancement in real-time 6D pose estimation technology. Then, we present the research: Color-Pair Guided Robust Zero-Shot 6D Pose Estimation and Tracking of Cluttered Objects on Edge Devices. Specifically, our approach eliminates the need for training on labeled object pose data, requiring only the object's mesh file and texture information as reference. The pipeline begins with object detection handled by a YOLO neural network, followed by the Segment Anything Model (SAM) to obtain precise instance masks. Central to our method is a novel, lighting-invariant color-pair feature descriptor extracted from texture contours. Instead of learning classification, we employ a robust similarity metric based on triangular geometry in the CIELAB space to establish reliable correspondences between the scene and the rendered model. The final pose estimation utilizes a voting scheme based on semantic triangles retrieved from a pre-built hash database, followed by a composite ICP optimization that jointly refines the alignment of individual semantic parts and the global structure. Finally, we integrate a lightweight tracking module that leverages optical flow filtered by our color-pair metric. By training on procedurally generated random shapes using viewpoint-invariant features, the tracker captures general geometric motion, enabling efficient, continuous 6D pose tracking for arbitrary objects without object-specific training. Looking forward, we identify two potential research trajectories in the domain of 6D object pose estimation: (1) zero-shot 6D object pose tracking, which extends the pose estimation problem into the temporal domain while maintaining independence from target-specific training data and mesh reference, and (2) mesh-free few-shot 6D pose estimation methodology that circumvents the requirement for explicit 3D mesh models. Our future research will pursue one of these directions to further advance our work in 6D pose estimation.
Topology optimized lattice core effects on the flexural behavior of Ti-6Al-4V sandwich structures produced by electron beam powder bed fusion (EB-PBF)
(2025-10-02) Bol, Eric David; Ramulu, Mamidala
Metal sandwich structures are utilized in aerospace applications that require high specific flexural strength and temperature resistance. Conventional fabrication methods, which involve joining operations (e.g. brazing, welding, bonding) to attach face sheets to a core structure, limit the geometric complexity that can be produced economically with sufficient quality. New additive manufacturing (AM) technologies, such as electron beam powder bed fusion (EB-PBF), present the ability produce a monolithic sandwich structure from Ti-6Al-4V with novel topology optimized cores. For this investigation, five lattice core geometries were selected, three triply periodic minimal surface (TPMS) types: Gyroid, Diamond, and Primitive, and two strut types: Octet and Kelvin. To demonstrate the unique capability of AM to optimize structures, two methods for functionally grading the core densities were applied based on a 4-point bend simulation. One grading method used a compliance minimization strategy (density-based), and the other a von Mises stress minimization strategy. After using waste powder from another additive process to produce 15 novel sandwich structures, the flexural properties were determined by ASTM C393 4-point bend experiments. This research revealed that the continuous grading of the core densities successfully delayed flexure yielding without increasing weight, and the TPMS core geometries exhibited a higher resistance to core buckling. The stress minimization strategy provided superior performance, while the Diamond core geometry attained the highest flexure yield stress. This work serves as a valuable contribution in the design for additive manufacturing of metal sandwich products, and highlights the sustainability of the EB-PBF process.
Built to Share: Modular Design Tools for Accessible Microfluidics and Decentralized Diagnostics
(2025-10-02) McLemore Craig, Cosette; Olanrewaju, Ayokunle O
Modern microfluidics offers transformative potential for global health, but realizing that promise requires tools that are not only capable, but also accessible, adaptable, and designed for sharing. This dissertation advances a modular design philosophy for decentralized diagnostics and fluidic autonomy, focusing on approaches that eliminate reliance on expensive infrastructure.Chapter 1 addresses a pressing global health challenge: therapeutic drug monitoring (TDM) for HIV treatment and prevention. I introduce RESTRICT and REACT, enzymatic assays that quantify pharmacologic activity of antiretroviral drugs, providing clinically validated, equipment-light alternatives to LC-MS/MS. These assays enable long-term adherence monitoring in low-resource and point-of-care settings and motivate the pursuit of self-driven microfluidic systems capable of automation without external pumps or power. Chapter 2 develops and formalizes the microfluidic chain reaction (MCR) as a programmable capillaric architecture. By framing MCRs in a circuit-based analytical model and producing open-source design tools, I bridge theoretical potential and practical deployment, enabling fluidic workflows to be encoded directly into chip geometry. This framework defines design rules, failure modes, and implementation strategies for autonomous fluid handling. Chapter 3 evaluates fabrication strategies for capillaric systems outside of cleanroom environments, focusing on cost, resolution, and ease of implementation. I assess low-cost LCD 3D printers alongside conventional DLP systems, documenting achievable feature dimensions, common print failures, and post-processing techniques. Practical guidance is provided for assembly, integration of capillary pumps, interfacing components, and adapting designs for accessible manufacturing. These methods lower the barrier for fabricating functional, programmable microfluidic circuits in resource-limited settings. Chapter 4 extends the capillarics toolkit by adapting two advanced cleanroom- fabricated geometries, phaseguides for sequential fluid routing and SCMs for reagent resuspension, into printable forms compatible with low-cost LCD 3D printers. I characterize their operation, identify design constraints imposed by printer resolution, and validate performance in fluid routing and pulse-shaped reagent delivery. A sharable STL library, failure mode atlas, and Python-based analysis scripts accompany these designs, enabling reproducibility and adaptation by other researchers. Together, these contributions establish a shareable toolbox of assays, geometries, fabrication methods, and analytical frameworks for building autonomous, modular microfluidic devices. The work shifts high-performance microfluidics from cleanroom exclusivity toward open, distributed manufacturing, broadening who can design, build, and deploy diagnostic systems in both research and global health contexts.
Vision and Control for Insect-scale Robots
(2025-10-02) Yu, Zhitao; Fuller, Sawyer B
This dissertation focuses on advancing vision and control for insect-scale robots, tackling two critical challenges: hovering and navigation. For hovering, the first study, presented in the TinySense paper (Best Student Paper Award at ICRA 2025), develops a lightweight, energy-efficient avionics system, integrating a pressure sensor and a tiny global shutter camera for state estimation. Validated on the Crazyflie drone, this system demonstrates robust hovering state estimation performance comparable to the Crazyflie built-in state estimator and the ground truth captured by Mocap (Motion capture) system. The second study introduces a gyroscope-free, visual-inertial flight control system for 10 mg robots that combines an accelerometer and tiny optic flow camera for effective wind rejection, inspired by biological sensory fusion in fruit flies. This approach, detailed in our Science Robotics paper, significantly reduces weight and power consumption, enabling stable flight even under wind disturbances. For navigation, we first introduce a biologically inspired, bilinear optic flow approximation method, suitable for confined-space navigation in environments where traditional mapping techniques like SLAM are infeasible due to speed, size, weight, and power (SSWaP) constraints. This method leverages learned optic flow patterns for heading stability within a corridor, as outlined in our IROS paper, demonstrating confined-space navigation without reliance on a pre-mapped environment. Building on this foundation, we next leverage the emergence of embedded event cameras—whose characteristics align well with the sensing constraints of insect-scale robots—to tackle the challenge of navigation in confined spaces. We work on developing a deep reinforcement learning framework that enables vision-based navigation using onboard event-based vision data alone. This approach has potential to achieve autonomous flight for insect-scale robots in confined spaces without external localization or mapping. Together, these contributions advance the potential for fully autonomous, insect-scale robotic platforms operating in real-world conditions.
Data-Driven Optimizations of Manufacturing Processes
(2025-10-02) Krishnan, Anand; Manohar, Krithika
Despite the success of data-driven techniques in achieving superhuman performance across various domains, significant challenges remain in applying these methods to manufacturing processes. Unlike fields with large-scale datasets like ImageNet, manufacturing lacks comparable datasets to train machine learning models for tasks such as directly estimating ergonomic risk. Additional challenges, including sensor failures, limited data availability, and the time-intensive nature of data collection, constrain the performance and practical usability of machine learning models in this domain. These limitations highlight the need for tailored approaches that leverage domain-specific data and methodologies to address challenges unique to manufacturing. Composite layup for small, complex parts is still done manually, presenting a compelling case for data-driven approaches due to the intricate motions and ergonomic risks involved. This work develops a data-driven ergonomic risk assessment system with a special focus on hand and finger activity to better identify and address ergonomic issues related to hand-intensive manufacturing processes. The system comprises a multi-modal sensor testbed designed to collect and synchronize data on operator upper body pose, hand pose, and applied forces. Ergonomic risk is assessed using the novel Biometric Assessment of Complete Hand (BACH) formulation to measure high-fidelity hand and finger risks, alongside industry-standard risk scores for upper body posture (RULA) and hand activity (HAL). Using the collected dataset, machine learning models are developed to automate RULA and HAL scoring, achieving over 95% classification accuracy and generalizing well to unseen participants. The assessment system provides ergonomic interpretability of manufacturing processes and can be used to mitigate risks through workplace optimization and posture corrections. Building on these human factors insights, this work investigates the transition from human-centered ergonomic assessment to large-scale manufacturing automation through data-driven methods. A hybrid correction framework is developed that combines Finite Element Model (FEM) simulations with experimental data, resulting in a computationally efficient approach for improved prediction accuracy. The approach achieves an average 91.9% reduction in RMSE compared to FEM predictions across eight actuator locations, with prediction uncertainties quantified to ensure model reliability. The corrected model is then applied to optimize actuator placement using QR decomposition of the displacement matrices, identifying optimal locations that achieve superior shape control performance compared to conventional placement strategies. Looking ahead, the automated ergonomic risk assessment system and adaptive tooling techniques developed in this work establish a comprehensive framework for data-driven manufacturing optimization that addresses both human factors and system performance. The research demonstrates pathways for technology translation from laboratory demonstration to industrial deployment, representing a holistic approach to modern manufacturing challenges that prioritizes worker safety while enabling advanced automation capabilities.
Development and Optimization of Novel Single Mode Electromagnetic Resonant (SMER) Techniques for Cryopreservation
(2025-10-02) Ma, Ruidong; Gao, Dayong
Since 1949, scientists have made significant efforts to advance cryopreservation techniques for a wide range of biological materials, including deoxyribonucleic acid (DNA), ribonucleic acid (RNA), proteins, biofluids, cells, tissues, and organs. Cryopreservation typically involves five major steps: cryoprotective agent (CPA) loading, cooling, storage, warming, and CPA unloading. Despite decades of progress, the field still relies heavily on manual, dropwise CPA addition and removal methods. Current dilution protocols often employ isotonic solutions or multi-step dilution approaches, which are labor-intensive and may not be optimized for different cell types. There is a critical need for fully automated systems that can perform CPA addition and removal in a controlled, cell-type-specific manner—taking into account factors such as volumetric flow rate and hypotonic solution selection to minimize osmotic stress. Furthermore, a post-dilution concentration step that eliminates the need for centrifugation would greatly enhance the clinical utility of cryopreserved cell suspensions, particularly in cellular therapy applications.For the cooling process, vitrification offers an effective strategy to prevent ice crystal formation, which is particularly important for preserving complex biological systems such as tissues and organs containing multiple cell types. However, conventional vitrification typically requires high concentrations of small-molecule cryoprotectants like dimethyl sulfoxide (DMSO) or glycerol, which can cause significant osmotic and toxic injury to cells and tissues. To address this challenge, recent efforts have focused on reducing the use of small-molecule CPAs in vitrification protocols. In this work, we introduce a novel approach utilizing high concentrations of polyvinylpyrrolidone (PVP) with varying molecular weights to minimize DMSO content while maintaining vitrification efficacy. This strategy demonstrated successful preservation outcomes in both cell suspensions and tissue samples. Preliminary experiments on mouse liver tissue confirmed the feasibility of this CPA formulation. Using this protocol, 4 mL of cell suspension could be directly plunged into liquid nitrogen for vitrification and subsequently rewarmed in a water bath, resulting in high post-thaw recovery and proliferation rates. Conventional water bath rewarming is inadequate for large vitrified samples due to its reliance on low-efficiency convective heat transfer. In contrast, electromagnetic (EM) waves can penetrate biological materials and enable volumetric heating, offering a more effective solution for rapid and uniform rewarming. To optimize EM-based rewarming, three key parameters must be considered: operating frequency, dielectric properties of the sample, and electric field intensity (determined by input power). A single-mode electromagnetic resonant (SMER) cavity was developed to concentrate energy through mode selection, enabling efficient and localized energy deposition. A generalized design methodology was established for constructing both electric and magnetic field–dominant cavities at their respective fundamental modes. To address the frequency drift caused by temperature-dependent changes in dielectric properties, an auto-resonance tracking system was implemented to maintain resonance during the warming process. This system also monitors the voltage standing wave ratio (VSWR) to quantify reflected power. The complete system was successfully applied to rewarm both 10 mL and 20 mL frozen and vitrified samples, demonstrating superior performance compared to conventional rewarming techniques. In addition to cell suspensions, this work extends to the cryopreservation of tissues and organs. Plans for future development are also outlined in this dissertation, including the advancement of next-generation cryoprotective agents, the design of an integrated perfusion system, and the construction of an improved electromagnetic cavity. The proposed cavity will incorporate features such as real-time temperature monitoring, user-friendly sample loading, antenna coupling optimization, electric field uniformity enhancement, and an integrated shaking platform to promote thermal homogeneity. Ultimately, this research aims to advance toward the successful cryopreservation and rewarming of whole organs.
An Architecture for Onboard Flight Control of a Sub-Gram Piezo-Actuated Aerial Vehicle
(2025-10-02) Kodey, Sriram; Fuller, Sawyer B
The development of agile, autonomous insect-scale aerial vehicles opens promising avenues for applications in environmental monitoring, search and rescue, and swarm robotics. However, the creation of such miniature platforms is hindered by stringent constraints on size, weight, and computational resources to achieve sensing, control, and actuation all within tight limits. This thesis presents a comprehensive architecture for onboard flight control of a sub-gram, piezo-actuated aerial vehicle. The design and implementation of a microcontroller-based high-voltage actuation system capable of generating precise signals for piezoelectric actuators is discussed in detail. A finite state machine (FSM) combined with a cascaded PID control architecture was developed for stabilization and maneuvering, utilizing real-time feedback from external motion capture systems operating at 240 Hz. Additionally, a lightweight and efficient logging infrastructure was developed to enable continuous recording of state estimates and control signals for post-flight analysis and debugging. Experimental results demonstrate the system's successful real-time generation of smooth, continuous sinusoidal waveforms for the piezoelectric actuators in response to pose feedback, as well as reliable capture of flight log data. Collectively, these results highlight the effectiveness of the proposed system and lay the foundation for control autonomy in insect-scale aerial robotics.
Data-Driven Modeling and Sparse Sensing for Nuclear Energy Systems
(2025-10-02) Karnik, Niharika; Manohar, Krithika
In nuclear energy systems, the integration of computational and interpretable data-driven models is essential for ensuring the safe and efficient operation of reactors and subsystems. These models leverage real-time sensor data to reconstruct critical variables such as temperature, pressure, and velocity, enabling precise monitoring, control, and optimization. By minimizing the need for costly physical experiments, computational models allow virtual testing of designs and operational strategies while addressing uncertainties and enhancing subsystem performance. Coupled with data-driven methodologies, they form the backbone of modern nuclear engineering, facilitating real-time decision-making and ensuring robust, reliable, and efficient system operations. Accurate reconstruction and monitoring in nuclear systems face challenges due to sensor noise and the impracticality of deploying extensive sensor arrays in harsh environments. This work addresses these limitations by introducing a data-driven framework for spatially constrained sensor placement, designed to minimize reconstruction errors and quantify noise-induced uncertainties. Using a greedy optimization algorithm, this approach ensures physically feasible configurations while maintaining high-fidelity reconstructions. These advancements are applied to critical scenarios, including fuel irradiation experiments, nuclear fuel test rod models, and steam generator subsystems, laying the groundwork for creating reliable and interpretable models for nuclear power plants (NPPs). This work further explores the effects of power transients on reactor core coolant dynamics, developing frameworks that adapt sensor placement and sampling intervals to changing operational states. The integration of classification tools like Linear Discriminant Analysis (LDA) with DMDc enables precise identification of transient regimes, ensuring robust monitoring and control. These contributions extend beyond individual subsystems, enabling the development of comprehensive digital twins for entire NPPs. By addressing noise, spatial constraints, power perturbations and transient conditions, this work establishes a foundation for advanced monitoring and control in nuclear systems. The proposed frameworks enable proactive decision-making, enhance safety, and improve operational efficiency, contributing to a new standard for intelligent, data-driven engineering solutions in the nuclear industry.
Evolution of Structure in Solid-State Microcellular and Nanocellular Polyetherimide (PEI) Foams as a Function of Carbon Dioxide Concentration
(2025-10-02) Sridhar, Santhosh; Kumar, Vipin
Evolution of Structures in PEI-CO2 System This study systematically investigates the evolution of structures in solid-state Polyetherimide (PEI) foams as a function of CO2 concentration across a saturation pressure range of 0.5 to 5.5 MPa at room temperature. The PEI-CO2 system produces a microcellular structure between the saturation pressure range of 0.5 (2.2 wt.% CO2) to 3 MPa (8 wt.% CO2), with cell sizes ranging from 1 to 5 μm. A rapid transition from a microcellular to a nanocellular (cell size ~ 10 nm) structure is observed between 3 and 3.5 MPa (10 wt.% CO2), which is unique to the PEI-CO2 system. The PEI-CO2 system produces a nanocellular structure between the saturation pressure range of 3.5 to 5.5 MPa (12.4 wt.% CO2) with average cell sizes ranging from 20 to 130 nm. At 5.5 MPa, the foams exhibit a uniform nanocellular structure with an average cell size consistently around 20 nm across various foaming temperatures. A probabilistic model based on cell nucleation density is presented to predict the size distributions of the critical radius of cell nuclei based on the Laplace Equation. The model explains the mechanism of the structural transition from microcellular to nanocellular through progressive activation of smaller flaws by increasing saturation pressure. Microcellular PEI foams exhibit a unique cellular structure, wherein secondary nanopores, ranging in size from 10 to 80 nm, develop on the cell walls of the primary microcells. This study experimentally characterizes the evolution of the secondary nanoporous structure as a function of primary cell expansion. The study comprises two parts: In Part I, the foaming temperature was varied independently at saturation pressures of 0.5 MPa, 2.5 MPa, and 3.3 MPa. In Part II, the foaming time was varied independently at different foaming temperatures under a saturation pressure of 1 MPa. The experimental results demonstrate that the growth of the primary cells directly influences the growth of the secondary nanopores. A modified classical nucleation theory framework based on strain energy due to primary cell expansion is presented to explain that the underlying nucleation mechanism of secondary nanopores is a coupled effect of polymer crazing and foaming. Investigating the Effect of Cell Size on the Toughness of PEI Foams This research is a collaborative effort involving Microcellular Plastics Lab, Meza Research Group in the Department of Mechanical Engineering, and the Multiscale Analysis of Materials & Structures (MAMS) Lab in the Department of Aeronautics and Astronautics. The title of the project is "Investigating Fundamental Toughness Mechanisms in Nanocellular Foams," which was funded by the National Science Foundation. The PI of the project is Dr. Lucas Meza from the Department of Mechanical Engineering. Dr. Vipin Kumar and Dr. Marco Salviato are the co-PIs from the Department of Mechanical Engineering and the Department of Aeronautics and Astronautics, respectively. This investigation was conducted in collaboration with Kush Dwivedi, a Ph.D. student in Mechanical Engineering. Complete details of all the tensile and fracture experiments, along with numerical simulations, are to be provided in Kush Dwivedi's Ph.D. dissertation. The author contributed to the development of process space to fabricate microcellular and nanocellular foams, the fabrication of foam specimens for mechanical tests, the preparation of mechanical test specimens, the SEM characterization of foams before mechanical testing, and the SEM characterization of fracture surfaces. Process space maps consisting of relative densities at different foaming temperatures were established for microcellular and nanocellular PEI foams. Process conditions were selected from the process space maps to fabricate microcellular PEI foams with cell sizes ranging from 3 to 5 μm and nanocellular PEI foams with cell sizes ranging from 15 to 40 nm. The relative densities of the foams ranged from 0.4 to 0.8. Uniaxial tensile and fracture tests were performed to investigate the effect of cell size on fracture toughness. Tensile behavior was characterized using true stress–strain curves, while fracture behavior was characterized using load–CMOD (Crack Mouth Opening Displacement) curves. True strain and CMOD measurements were obtained using Digital Image Correlation (DIC). In general, nanocellular foams demonstrated significantly higher toughness than microcellular foams at equivalent relative densities. These findings contradict the classical scaling law that predicts a reduction in fracture toughness with decreasing cell size. Notably, nanocellular foams with a relative density of 0.8 exhibited fracture toughness comparable to that of unprocessed PEI. Post-test fracture surface analysis was conducted using SEM. Regions of stable crack propagation exhibited cell wall stretching, indicative of ductile failure. Conversely, unstable crack propagation regions exhibited flat fracture surfaces, characteristic of brittle failure. Overall, nanocellular foams exhibited larger regions of stable crack growth with cell wall stretching. This indicates plastic deformation occurring locally at the nanoscale level. Therefore, ductility at the nanoscale level leads to toughness enhancement, which is the underlying reason for the disruptive fracture behavior of the nanocellular foams. Advanced Methods to Produce Skinless Solid-State Foams Previous studies have shown that nanocellular PEI foams have an open-celled porous structure. However, access to these porous structures is limited by the presence of the solid skin layer. A novel plasma treatment-based solid-state process was developed in collaboration with Ankush Nandi (Ph.D. student, ME) from the Vashisth Lab in the Department of Mechanical Engineering to produce skinless foams. The author contributed to designing, executing the experiments, and conducting SEM analysis. The author and Ankush Nandi contributed to tuning the plasma system parameters to achieve solid-state foaming. This process involves plasma treatment of CO₂-saturated polymer sheets (with thicknesses ranging from 0.5 to 1.6 mm) using a commercially available low-temperature air plasma system. The resulting foams exhibit a porous surface on the plasma-treated side, with a gradient foam structure extending into the cross-section. Skinless microcellular foams were successfully fabricated in the PC–CO₂ system at saturation pressures of 1 MPa and 5 MPa. Additionally, skinless nanocellular foams with an average cell size of 90 nm were produced in the PEI–CO₂ system. Selective foaming was achieved on the 15 μm-thick skin of a prefabricated open-cell porous nanocellular PEI sheet by partially re-saturating the skin with CO2 and subsequently treating it with plasma. The SEM images revealed pores within the skin region and on the treated surface. The acetone-based dye penetration test confirmed that the treated surface is permeable, and the skin is now porous and interconnected with the core. Thus, we successfully demonstrated a new technique to selectively make the solid skin porous, thereby enabling a new way to access the porous structure at the core. Therefore, the plasma treatment-based solid-state foaming process is a promising method for producing skinless solid-state foams to access the core.
Development of an automated human whole-blood-based carbon nanotube paper-composite (CPC) hemostasis evaluation system
(2025-10-02) Jin, Ye; Gao, Dayong
Hemostasis is a complex series of physiological reactions that occur when bleeding happens or a blood vessel is damaged. It functions to seal the injury site and stop bleeding. Hemostasis involves multiple interrelated steps, including platelet plug formation, coagulation, and fibrinolysis involving plenty of blood components, including platelets, coagulation factors, and erythrocytes (red blood cells). To date, no individual or model can fully explain all the reactions, mechanisms, and activities of the hemostatic process. Compared to the intricacies of hemostasis, effective hemostasis management is essential across a range of medical fields, including screening for cardiovascular diseases, monitoring anticoagulant therapy, blood transfusion management, preoperative hemostasis evaluation, Extracorporeal blood circuit therapy, screening for pre-existing coagulopathies. Therefore, a rapid, accurate and comprehensive hemostasis assessment is crucial for patient management and further hemostasis research. Currently, the most widely used routine hemostasis assays require separate tests for every hemostasis function. There are some novel global hemostasis assessments, including thromboelastography (TEG) and thromboelastometry (ROTEM). Limitations on recent assays, including partial or incomplete information on hemostasis function evaluation, high cost, time-consuming, labor-intensive, requiring professional medical personnel to conduct, analyze results. In this dissertation, an automated whole blood capacitance assessment using a carbon nanotube paper-composite(CPC) sensor is developed. Its automated module and integrated fluctuation analysis algorithms will also be presented.The automated whole-blood capacitance assessment system utilizes a carbon nanotube paper-composite (CPC) sensor for coagulation evaluation. Carbon nanotubes are emerging materials known for their high sensitivity, attributed to their excellent electrical conductivity and intricate surface structure, applied in many biosensors. In this study, carbon nanotube are used as the electrodes of a capacitance sensor to monitor changes in blood sample permittivity during the coagulation process the applied electric field. Importantly, the electrodes do not directly contact the blood samples. Compared to TEG, ROTEM, and other global or dielectric-based coagulation sensors, this system is low-cost, reusable, and portable. From the capacitance signal, multiple parameters can be analyzed and correlated with various hemostatic components, including coagulation function (factors), platelets, and erythrocytes(RBCs). The results also demonstrated strong agreement with the thromboelastography assay. Further investigations into the potential functions and advantages of this system are also presented in this dissertation. The most commonly used routine hemostasis assays are typically limited to hospitals or clinical laboratories. These limitations arises primarily from two factors. First, the devices used in conventional hemostasis assays are non-portable and the testing process is complex. Routine tests require individual assays for each hemostatic parameter, with each test depending on specialized equipment. Also, these assays require trained medical personnel to perform the procedures and interpret the results, which often provide only partial information that must be analyzed by a medical professional. These two factors make the portability of conventional hemostasis assays nearly unfeasible. The development of this automated whole blood capacitance-based hemostasis evaluation system aims to address this issue by enabling portable hemostasis assessment. Automatic module can complete the entire process from blood sample aspiration to post-test disposal without manual intervention. Automatic assessment will reduce the requirements for medical personnel and helps standardize the testing protocol. The system's ability to perform multi-parameter and automated assessments makes hemostasis evaluation applicable in portable, cost-effective, and potentially home-use scenarios. Furthermore, this dissertation presents a novel fluctuation analysis of the capacitance signal. Fluctuation signal can avoid the influence of some noise on the capacitance baseline. Future improvements to the system will focus on further fluctuation analysis and more coagulation parameters correlations. A more compressed and industrially designed automatic module is also in the plan for the portable and automated objective.
Study of Fracture Toughness and Notch Toughness under Impact loading of Electron Beam Melted Ti-6Al-4V alloy
(2025-10-02) Tambi, Vidit; Mamidala, Ramulu
This thesis presents a comprehensive investigation into the fracture toughness and the notch toughness behavior of Electron Beam Powder Bed Fused (EB-PBF) Ti-6Al-4V alloy, with particular focus on the effects of build orientation, key mechanical properties and microstructural characteristics. Vertical compact tension specimens as well as Horizontal and vertical Charpy Impact specimens were fabricated using optimized processing parameters. Notch toughness was evaluated using instrumented Charpy impact tests, while fracture toughness was measured using linear-elastic fracture mechanics on fatigue-precracked specimens. The fracture surfaces were examined using profilometry, scanning electron microscopy, microhardness measurements, and microstructural analysis. The findings were correlated with the mechanical properties of similarly fabricated specimens under identical conditions.Vertical specimens demonstrated superior toughness performance across both test regimes. In fracture toughness testing, Y-Z specimens exhibited the highest toughness (65.81 MPa√m), while X-Z showed the lowest (63.10 MPa√m). Post-machining generally improved toughness values by mitigating surface defects and enhancing crack resistance. In impact testing, vertical specimens absorbed ~50% more energy than horizontal specimens due to greater ductility, lower microhardness, and crack propagation paths that intersect prior-β grains more frequently. Surface profilometry confirmed ~35% higher shear lip heights, ~50% larger shear areas, and ~15% rougher cracking regions in vertical specimens, indicating increased plastic deformation. The study highlights the critical role of build orientation and post-processing in tailoring the toughness of EB-PBF Ti6Al4V, offering valuable guidance for design and qualification of AM components in structural applications.
Development and Validation of a Modular, Realistic IV Placement Training Model (PractIV)
(2025-10-02) Cowin, Liam Christopher; Reinhall, Per
This thesis presents the development of PractIV, a modular peripheral intravenous (PIV) training simulator designed to improve realism, adaptability, and integration of IV placement training for healthcare professionals. The research project is a collaboration with the University of Washington (UW) Mechanical Engineering 'Engineering in Health' (EIH) Program, and the Center for Research in Education and Simulation Technologies (CREST) at UW. The goal of this work is to develop a modular IV arm trainer specifically designed to comply with the Modular Healthcare Simulation and Education System (MoHSES™) physical connection standards, enabling seamless integration with existing manikins such as the Advanced Joint Airway Management System (AJAMS), Both previously developed by CREST. PractIV addresses key limitations in existing IV training tools, which often lack anatomical realism and fail to prepare learners for challenging patient scenarios. By combining insights from clinical practice, mechanical design, and additive manufacturing, this work contributes to the fields of medical simulator development and IV training.
Data-driven and experimental modeling of an oscillating surge wave energy converter
(2025-10-02) Lydon, Brittany; Polagye, Brian; Brunton, Steven
Wave energy converters (WECs) are devices that harness power from ocean waves with useful applications including powering remote communities and desalination plants. Oscillating surge WECs (OSWECs) are a promising sub-category of WECs because they can absorb power over a wide range of wave frequencies and rely less on resonance than other WEC archetypes. However, OSWECs can exhibit complex dynamics that can be difficult to accurately model without significant computational burden. Often, this results in either low-fidelity, potentially inaccurate linear models or high-fidelity numerical simulations that are too computationally expensive for operational use. Scaled experimental modeling can address these issues, however there are limitations when the scaled device does not achieve exact similarity to the full-scale system. When this occurs, data from the experiment may not be representative of the full-scale system and could cause errors when predicting device behavior and performance. This work explores data-driven and experimental methods that address these limitations. Specifically, we use data-driven algorithms that balance accuracy and computation speed to build accurate system models of OSWEC dynamics. We address realistic conditions such as noisy signals and nonlinear events, including wave overtopping. Additionally, we demonstrate experimental techniques that mitigate common scale effects using control, providing an efficient method to investigate and address hardware limitations. To bridge the gap between model accuracy and computation speed, we propose the use of dynamic mode decomposition (DMD) as a purely data-driven technique that can generate an accurate and computationally efficient model of OSWEC dynamics. Specifically, we model and predict the behavior of an OSWEC in mono- and polychromatic seas without an equation of motion or knowledge of the incident wave field. We generate data with the open-source code WEC-Sim, then evaluate how well DMD can describe past dynamics and predict future behavior. We consider realistic challenges including noisy sensors, weakly nonlinear dynamics, and irregular wave forcing. Specifically, by using an extension of DMD we reduce the effect of noise on our system and significantly increase model accuracy outside the training region. Additionally, by introducing time delays we accurately describe weakly nonlinear dynamics, even though DMD is a linear algorithm. Finally, we use Optimized DMD (optDMD) to model OSWEC behavior in response to irregular waves. While optDMD accurately models training data, future prediction remains inaccurate, demonstrating the limits of modeling efforts without access to information about the incident wave field. These findings provide insight into the use of DMD, and its extensions, on systems with limited time-resolved data and present a framework for applying similar analysis to lab- or field-scale experiments. Limitations of linear models are magnified in energetic seas where extreme events, such as overtopping and slamming, and large-amplitude motions can introduce significant nonlinearities in the dynamics. To address this, we use the sparse identification of nonlinear dynamics (SINDy) algorithm to build parsimonious reduced-order models of WEC dynamics from nonlinear experimental data. Specifically, we learn interpretable, nonlinear models to describe OSWEC dynamics in response to varying amounts of overtopping. The SINDy models are trained on experimental data from a laboratory-scale device operating in regular waves. These models describe the surge and heave forces acting on the OSWEC flap using only kinematic time series. While surge force is relatively unaffected by overtopping severity, heave force shows significant nonlinear behavior with increased overtopping. Regardless of complexity, SINDy generates accurate models of both surge and heave forces in tests ranging from no overtopping to severe overtopping, especially when compared to linear techniques. We explore how well these models generalize to other experiments and find that we can use a single model to describe surge force over the full range of overtopping, while heave force requires a continuum of models with varying degrees of nonlinearity to accurately describe the full range of dynamics. Overall, this work supports the use of SINDy to generate accurate models of WEC dynamics, especially when strong nonlinearities are present. While scaled-model testing is a promising method for collecting high-quality data, scale effects can reduce result accuracy. Here, we identify, quantify, and mitigate scale effects of the same laboratory-scale OSWEC that would not be equally present in full-scale systems, including reduced buoyancy due to sensor weight and driveline friction. Specifically, after we characterize the buoyancy and friction torque profile, we use a servomotor to emulate additional buoyancy and offset friction using real-time position and torque feedback. We find that emulating additional buoyancy substantially improves system performance by adjusting the natural period of the device, essentially implementing phase control to operate closer to resonance. Because of this, sensor weight should be considered in scaled-devices to ensure performance is representative of the full-scale system. However, friction, while appreciable in comparison to the prescribed control torque, has a limited effect on system kinematics and performance. This is because the frictional torque is in phase with velocity and therefore acts as a damper in the same manner as the power takeoff. These examples demonstrate the importance of considering scale effects in experimental testing and introduces novel methods to address and mitigate these effects using feedback control, providing a low-cost and efficient method to investigate and address hardware limitations. With this work, we aim to further WEC development by introducing data-driven and experimental methods that expand the existing WEC modeling tools and provide low-cost and efficient methods for accurate scale model testing.

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